Accurate physical simulation is crucial for the development and validation of control algorithms in robotic systems. Recent works in Reinforcement Learning (RL) take notably advantage of extensive simulations to produce efficient robot control. State-of-the-art servo actuator models generally fail at capturing the complex friction dynamics of these systems. This limits the transferability of simulated behaviors to real-world applications. In this work, we present extended friction models that allow to more accurately simulate servo actuator dynamics. We propose a comprehensive analysis of various friction models, present a method for identifying model parameters using recorded trajectories from a pendulum test bench, and demonstrate how these models can be integrated into physics engines. The proposed friction models are validated on four distinct servo actuators and tested on 2R manipulators, showing significant improvements in accuracy over the standard Coulomb-Viscous model. Our results highlight the importance of considering advanced friction effects in the simulation of servo actuators to enhance the realism and reliability of robotic simulations.

翻译：精确的物理仿真对于机器人系统中控制算法的开发与验证至关重要。强化学习领域的最新研究显著利用了大规模仿真来生成高效的机器人控制策略。目前最先进的伺服执行器模型通常无法捕捉这些系统中复杂的摩擦动力学特性，这限制了仿真行为向实际应用的迁移能力。本研究提出了扩展摩擦模型，能够更精确地模拟伺服执行器的动力学行为。我们对多种摩擦模型进行了全面分析，提出了一种基于摆锤测试台记录轨迹的模型参数辨识方法，并演示了如何将这些模型集成到物理引擎中。所提出的摩擦模型在四种不同的伺服执行器上进行了验证，并在2R机械臂上进行了测试，结果显示其精度较标准的库仑-粘性摩擦模型有显著提升。我们的研究结果强调了在伺服执行器仿真中考虑高级摩擦效应对于提升机器人仿真真实性与可靠性的重要意义。